Diabetes, obesity & metabolism · 2026
Artificial Intelligence in Screening and Grading Diabetic Eye Diseases: A Systematic Review From Algorithms to Clinic.
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Zhou Jiamin, Hu Chen, Chen Jiaqi, Liu Jinshen, Li Xiang
Аннотация
SIGNIFICANCE: This systematic review comprehensively synthesises the progress of artificial intelligence in the grading diagnosis of diabetes-related ocular diseases, with a specific focus on the translational gaps from algorithm development to clinical implementation.
PURPOSE: This study aimed to systematically review and summarise the technological evolution, advantages of clinical application, and limitations of artificial intelligence in the grading diagnosis of diabetes-related ocular diseases (such as diabetic retinopathy and diabetic macular edema), clarify its clinical translation pathways and propose future research directions.
METHODS: A systematic literature review was conducted according to the PRISMA guidelines. Relevant English-language articles published between 2021 and 2025 were searched in databases such as the Web of Science using Boolean operators. A total of 74 core publications were included, including AI algorithm types, performance evaluations, clinical validations and translational research.
RESULTS: AI has formed a technical system primarily based on supervised learning with integrated algorithms for grading the diagnosis of diabetes-related eye diseases, demonstrating significant advantages over traditional manual diagnosis in screening efficiency, diagnostic consistency and healthcare accessibility. However, limitations remain in the identification of early-stage lesions, diagnosis in multidisease comorbidity scenarios and cross-device generalisation. Optimisation strategies include data augmentation using generative adversarial networks, multimodal fusion and the enhancement of interpretability. In clinical translation, screening models based on portable devices have emerged; however, challenges persist regarding data security and standardised validation.
CONCLUSIONS: Artificial intelligence holds significant clinical value and application potential in the graded diagnosis of diabetes-related ocular diseases, improving screening efficiency and consistency, particularly in resource-limited settings. Future efforts should focus on enhancing algorithmic adaptability in complex scenarios, promoting deeper integration of technology with clinical workflows, and establishing robust data security and validation standards to facilitate large-scale and high-quality clinical implementation.
PURPOSE: This study aimed to systematically review and summarise the technological evolution, advantages of clinical application, and limitations of artificial intelligence in the grading diagnosis of diabetes-related ocular diseases (such as diabetic retinopathy and diabetic macular edema), clarify its clinical translation pathways and propose future research directions.
METHODS: A systematic literature review was conducted according to the PRISMA guidelines. Relevant English-language articles published between 2021 and 2025 were searched in databases such as the Web of Science using Boolean operators. A total of 74 core publications were included, including AI algorithm types, performance evaluations, clinical validations and translational research.
RESULTS: AI has formed a technical system primarily based on supervised learning with integrated algorithms for grading the diagnosis of diabetes-related eye diseases, demonstrating significant advantages over traditional manual diagnosis in screening efficiency, diagnostic consistency and healthcare accessibility. However, limitations remain in the identification of early-stage lesions, diagnosis in multidisease comorbidity scenarios and cross-device generalisation. Optimisation strategies include data augmentation using generative adversarial networks, multimodal fusion and the enhancement of interpretability. In clinical translation, screening models based on portable devices have emerged; however, challenges persist regarding data security and standardised validation.
CONCLUSIONS: Artificial intelligence holds significant clinical value and application potential in the graded diagnosis of diabetes-related ocular diseases, improving screening efficiency and consistency, particularly in resource-limited settings. Future efforts should focus on enhancing algorithmic adaptability in complex scenarios, promoting deeper integration of technology with clinical workflows, and establishing robust data security and validation standards to facilitate large-scale and high-quality clinical implementation.